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SciSciNet: A large-scale open data lake for the science of science research
The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how scienc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235093/ https://www.ncbi.nlm.nih.gov/pubmed/37264014 http://dx.doi.org/10.1038/s41597-023-02198-9 |
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author | Lin, Zihang Yin, Yian Liu, Lu Wang, Dashun |
author_facet | Lin, Zihang Yin, Yian Liu, Lu Wang, Dashun |
author_sort | Lin, Zihang |
collection | PubMed |
description | The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field. |
format | Online Article Text |
id | pubmed-10235093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102350932023-06-03 SciSciNet: A large-scale open data lake for the science of science research Lin, Zihang Yin, Yian Liu, Lu Wang, Dashun Sci Data Data Descriptor The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field. Nature Publishing Group UK 2023-06-01 /pmc/articles/PMC10235093/ /pubmed/37264014 http://dx.doi.org/10.1038/s41597-023-02198-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Lin, Zihang Yin, Yian Liu, Lu Wang, Dashun SciSciNet: A large-scale open data lake for the science of science research |
title | SciSciNet: A large-scale open data lake for the science of science research |
title_full | SciSciNet: A large-scale open data lake for the science of science research |
title_fullStr | SciSciNet: A large-scale open data lake for the science of science research |
title_full_unstemmed | SciSciNet: A large-scale open data lake for the science of science research |
title_short | SciSciNet: A large-scale open data lake for the science of science research |
title_sort | sciscinet: a large-scale open data lake for the science of science research |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235093/ https://www.ncbi.nlm.nih.gov/pubmed/37264014 http://dx.doi.org/10.1038/s41597-023-02198-9 |
work_keys_str_mv | AT linzihang sciscinetalargescaleopendatalakeforthescienceofscienceresearch AT yinyian sciscinetalargescaleopendatalakeforthescienceofscienceresearch AT liulu sciscinetalargescaleopendatalakeforthescienceofscienceresearch AT wangdashun sciscinetalargescaleopendatalakeforthescienceofscienceresearch |